A comprehensive analysis of the oncogenic and prognostic role of TBC1Ds in human hepatocellular carcinoma

TIMER database analysis

The Tumor Immune Estimation Resource (TIMER) is a comprehensive database that provides systematic analysis of gene expression and immune cells infiltrates in 32 different types of cancers (https://cistrome.shinyapps.io/timer/) from The Cancer Genome Atlas (TCGA). TBC1Ds were input into the TIMER database and the expression difference of TBC1Ds were observed between tumor and adjacent normal tissues for the different tumors or specific tumor subtypes in the TCGA. The “Immune-Gene” module of the TIMER database was used to verify the association between TBC1D expression and tumor-infiltrating immune cells, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells (DCs), in HCC.

UALCAN database analysis

UALCAN (http://ualcan.path.uab.edu/) is an interactive web resource, based on TCGA, for analyzing the relative gene expression in tumor tissues and normal tissues. In this study, UALCAN was used to analyze the relative mRNA expression of TBC1Ds across tumor grades. The promoter methylation levels of TBC1Ds in HCC were also analyzed with this tool.

GEPIA database analysis

Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/) is a newly developed interactive database for gene expression analysis of tissue samples from the TGCA and GTEx projects. This study used GEPIA to analyze the relative gene expression of TBC1Ds in HCC tumor tissues and paratumor tissues. The correlation between TBC1Ds and survival in patients with HCC was also explored using GEPIA. The correlation analysis of TBC1Ds and AFP, PDL1 and CTLA4 were also performed with the GEPIA database.

cBioPortal database analysis

The cBioPortal database (http://www.cbioportal.org/) is an open-access web tool for analyzing multidimensional cancer genomics datasets. In this study, the multidimensional analysis included mutation, copy number variation (CNV), and association of TBC1D mutations with clinical features in HCC.

STRING and metascape database analysis

The STRING (search tool for the retrieval of interacting genes/proteins) is a database of interactions between genes or proteins (https://cn.string-db.org/). In this study, STRING was used to predict possible protein-protein interaction networks between TBC1Ds and associated genes. The Metascape database (https://metascape.org/gp/index.html#/main/step1) is a web-based tool that combines gene, interactome, and functional enrichment analyses. This study used the Metascape database to perform enrichment analyses of the functional roles of TBC1Ds and their predicted associated genes.

Human protein atlas database analysis

The Human Protein Atlas is a Swedish-based program that maps all the human proteins in cells, tissues and organs using an integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics and systems biology (https://www.proteinatlas.org/). This study used the Human Protein Atlas to explore the protein levels of TBC1Ds in tumor and paratumor tissues of patients with HCC.

Kaplan–Meier plotter database analysis

The Kaplan–Meier plotter database (http://kmplot.com/analysis/) provides data analysis of 10,461 clinical samples of patients, including gastric cancer, breast cancer and HCC samples. In this study, the Kaplan–Meier plotter database was used to examine the association between TBC1Ds and the relapse free survival (RFS) of patients with HCC.

Immunohistochemistry

The HCC sections were dewaxed at 60 °C for an hour, then soaked through with a dimethylbenzene and ethanol solution. Sections were then incubated with rabbit polyclonal antibody against TBC1D14 (all from Abclonal) at 1/200 dilution overnight at 4 °C and antibody conjugated with peroxidase (HRP; 1:500 dilution) at room temperature for 2 h, then covered by 3,3-diaminobenzidine (DAB). HCC histologic sections stained for TBC1D14 were purchased from Servicebio.

Western blotting

Proteins were extracted from cells with western blotting kit according to manufacturer’s instructions (Beyotime, Shanghai, China). Then the protein solutions were separated on 10% SDS-PAGE gels, and transferred to a PVDF membrane (Millipore). After that, the membranes were blocked for 2 h at room temperature and incubated with TBC1D8 (#sc-376637, 1:1,000; Santa Cruz Biotechnology, Dallas, TX, USA) or TBC1D14 (#ab235544, 1:1,000; Abcam, Cambridge, UK) antibodies overnight at 4 °C. The membranes were visualized with an ECL detection system after incubation with secondary antibody at room temperature for 2 h.

Cell culture and transfection

Human HCC cell line (Hep3B) was purchased from the Shanghai Institute of Biochemistry and Cell Biology and cultured in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (WISENT, Shanghai, China). Before the cells were harvested, they were cultured at 37 °C with 5% CO2. The Cas9 expression construct pLV1-CMV-Cas9-Puro-U6-sgRNA was used for the expression of sgRNAs in Hep3B cells; sgRNA targeting TBC1D8 was 5′-AAATGGAGCGACCCGTGCAT-3′, and sgRNA targeting TBC1D14 was 5′-GGAATCCCTCCAAGTGTGAG-3′. Empty pLV1-CMV-Cas9-Puro-U6-sgRNA plasmid was used as a control (sgNC). Puromycin (1 μg/ml, Merck, 540,411) was added 24 h after transfection. Cells were collected 72 h after transfection for subsequent experiments.

Cell proliferation and migration assays

The EdU Assay Kit (Beyotime, Shanghai, China) and CCK8 Kit (Vazyme, Nanjing, China) were used to examine the growth ability of HCC cells. For the EdU assay, Hep3B cells were seeded into six-well plates at a 4 * 10⁵ cells/well density and incubated with EdU according to the manufacturer’s instructions. For the CCK8 assay, Hep3B cells were seeded into 96-well plates at a density of 1,000 cells/well, and five multiple wells were designed for each group. After incubation for 24, 48 and 72 h, the CCK8 solution was added and the cell viability were measured according to manufacturer’s instructions.

For the wound healing assay, Hep3B cells were seeded into six-well plates at a density of 4 * 10⁵ cells/well with serum-free medium. Then, the cells were scratched with 200 µl pipette tips. Cell migration was observed by photography in an Olympus CKX41 inverted microscope at 0 and 48 h.

Transcriptional level of TBC1Ds in patients with HCC

Six TBC1D factors were found to have vital functions in autophagy. The TIMER 2.0 database was used to investigate the transcriptional levels of TBC1Ds in cancers. Compared with the mRNA of TBC1Ds in normal samples, the mRNA of TBC1Ds were strongly upregulated in a variety of cancers including colon cancer, cholangiocarcinoma and pancreatic cancer. The most important finding was that the mRNA levels of all six TBC1D members--TBC1D1, TBC1D7, TBC1D8, TBC1D9b, TBC1D14 and TBC1D25—were significantly upregulated in HCC tissues and cholangio carcinoma (CHOL) tissues (Fig. 1A and Fig. S1). The Gene Expression Omnibus (GSE102079) database also confirmed the upregulated mRNA levels of TBC1Ds in HCC tissues compared to paratumor tissues (Fig. S2). To further verify the results, the mRNA levels of TBC1Ds were also explored in the UALCN dataset and the expression levels of TBC1D1, TBC1D7, TBC1D8, TBC1D9b, TBC1D14 and TBC1D25 were significantly higher in HCC than in normal liver tissues (Fig. 1B). These results all indicated that TBC1Ds are abnormally upregulated in HCC.

Verified protein levels of TBC1Ds in human HCC tissues

The Human Protein Atlas database and immunohistochemistry technology were employed to demonstrate the protein levels of TBC1Ds in HCC. As shown in the graph, TBC1D1, TBC1D8, TBC1D9b, TBC1D14 and TBC1D25 were highly expressed in HCC tissues but moderately expressed in normal liver tissues. However, there were no significant differences in TBC1D7 expression between HCC and normal tissues (Fig. 2). The IHC value was also analyzed and the results are shown in Fig. S3. These results further verified the abnormal upregulation of TBC1Ds in HCC.

Relationship between the expression of TBC1Ds and the clinicopathological parameters of patients with HCC

Using the GEPIA dataset, the mRNA levels of TBC1Ds were also analyzed at different cancer stages of HCC. The results showed that the mRNA levels of TBC1D1, TBC1D7, TBC1D8 and TBC1D14 significantly varied between different cancer stages, whereas the mRNA levels of TBC1D9b and TBC1D25 did not (Fig. 3A). The mRNA transcription levels of TBC1Ds were also found to be significantly upregulated as the tumor grades increased (Fig. 3B). These results indicated that TBC1Ds are highly expressed in HCC and may serve as vital biomarkers for HCC diagnosis.

The alpha fetoprotein (AFP) is one of the most widely used biomarkers for the clinical diagnosis of HCC, and levels of serum AFP are closely correlated with tumor development (Heimbach et al., 2018; Wong, Ahmed & Gish, 2015; Li et al., 2009). The mRNA levels of TBC1D1, TBC1D7 and TBC1D9b were significantly positively correlated with the mRNA level of AFP (Fig. 3C) in the GEPIA database. However, the association between TBC1Ds and AFP still needs to be validated in future experiments.

TBC1D expression was positively correlated with patient outcome

This study also explored the critical efficiency of TBC1Ds in the survival of patients with HCC. The GEPIA tool showed that high expressions of TBC1D1, TBC1D7 and TBC1D9B were significantly correlated with poor overall survival (OS) of patients with HCC. A disease-free survival analysis data (DFS) showed a significant correlation between high TBC1D8 and TBC1D14 expression and poor prognosis of patients with HCC (Fig. 4). However, only the high transcriptional level of TBC1D8 was significantly correlated with the relapse free survival (RFS) of patients with HCC (Fig. S4). These results suggested the prognostic significance of TBC1D expression and its close relationship with the clinical characteristics of HCC.

Genomic alterations of TBC1Ds in HCC

To investigate the mutations of TBC1D genes in HCC, the cBioPortal database was used to analyze the DNA sequencing data from patients with HCC. The TBC1Ds were altered in 305 (88%) samples of the 348 total HCC patient samples (Fig. 5A). Notably, single mutations of both TBC1D1 and TBC1D7 were found in almost half of the patients with HCC (Fig. 5B). The alterations involved mRNA missense mutation, splice mutation, truncating mutation, amplification, deep deletion, high mRNA and low mRNA. The most common alteration of TBC1D1, TBC1D8, TBC1D14 and TBC1D25 was shallow deletion, and the Gain was the most common alteration of TBC1D7 and TBC1D9b (Fig. 5C). Moreover, the frequency distribution of TBC1D CNV in HCC patients at different cancer stages suggested that TBC1D CNV alteration was an early event with a high frequency of occurrence in HCC (Fig. 5D). The methylation levels of TBC1D promotors were then examined because the hypermethylation of promoters may contribute to upregulated mRNA levels of oncogenes. The results showed that only the promoter methylation level of TBC1D1 was significantly upregulated in HCC tissues (Fig. S5).

Functions and pathways of TBC1Ds and interacting genes in HCC

To further investigate the underlying molecular mechanism of the TBC1D genes in HCC progression, the STRING tool was used to construct the network for TBC1Ds and the 48 most frequently interacting neighbor genes. The results showed that autophagy-related genes, including TSC1, TSC2, ATG3 and ATG7, were predicted to closely interact with TBC1Ds (Fig. 6A). The functions and networks of TBC1Ds and significantly associated genes were predicted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) in the Metascape database. A gene set enrichment analysis (GESA) predicted the functional roles of the genes based on their molecular functions and biological processes. The results showed that GO:0051020 (GTpase binding), GO:0019901 (protein kinase binding), GO:0008047 (enzyme activator activity), GO:0010506 (regulation of autophagy), GO:0006914 (autophagy), GO:0031669 (cellular response to nutrient levels) and GO:0032006 (regulation of TOR signaling) were significantly regulated by the alterations of TBC1Ds in HCC (Figs. 6B and 6C). A KEGG analysis defined the pathways related to the functions of TBC1D alterations and the frequently altered neighbor genes. A total of seven pathways related to the functions of TBC1Ds in HCC were found through the KEGG analysis (Fig. 6D). Among these pathways, has04140: (Autophagy-animal), hsa04152: (AMPK signaling pathway), hsa04150: (mTOR signaling pathway) and hsa04137: (mitophagy-animal) have been thoroughly studied in the tumorigenesis and pathogenesis of HCC (Chao et al., 2022; Bi et al., 2021).

Relevance between TBC1D expression and immune infiltration in HCC

The tumor microenvironment (TME) has been well established as an important mechanism influencing tumor initiation, progression and metastasis. Tumor-infiltrating immune cells (TIICs) are a significant component of the TME. To determine the effects of TBC1Ds on TIICs, the TIMER2.0 database was used to analyze the relationship between TBC1Ds and immune cell markers in HCC. A series of analyses showed that TBC1Ds, including TBC1D1, TBC1D7, TBC1D8, TBC1D9b, TBC1D14 and TBC1D25, were significantly correlated with the immune infiltration of B cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. However, only TBC1D1, TBC1D8 and TBC1D25 were significantly correlated with the immune infiltration of CD8+ T cells (Fig. 7). Copy number alterations of TBC1D7 and TBC1D25, which may include arm-level gain and high amplification, were significantly correlated with the infiltrating levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils and dendritic cells. High amplification of TBC1D14 also showed a significant correlation with the infiltrating levels of macrophages (Fig. 8). To further explore the role of TBC1Ds in the tumor immune microenvironment of HCC, the correlation between the mRNA levels of TBC1Ds and the immune checkpoint receptors of PDL1 and CTLA4 was examined. The results showed that the mRNA levels of TBC1D1, TBC1D14 and TBC1D25 were significantly correlated with PDL1 expression and the mRNA levels of TBC1D1, TBC1D8, TBC1D14 and TBC1D25 were significantly correlated with CTLA4 expression (Fig. S6). All these results indicated that TBC1Ds may participate widely in HCC immune cell infiltration and play a vital role in the occurrence and progression of HCC.

Knockout of TBC1D8 and TBC1D14 significantly suppressed the malignant feature of HCC cells in vitro

The above results indicated that TBC1Ds may be an independent factor for predicting the prognosis of patients with HCC. To further investigate the role of TBC1Ds on the malignant feature of HCC, we knockout the expression of TBC1D8 and TBC1D14 in Hep3B cells as these two genes play vital function in other types of cancers (Chen et al., 2019; Lu et al., 2022) and showed relative tighter relationship to the prognosis of patients with HCC. In the present study, Western blot analysis examined knockout efficiency (Fig. 9A) and CCK8 results showed that knockout of TBC1D8 and TBC1D14 both significantly suppressed the proliferation rate of Hep3B cells (Fig. 9B). Wound healing and EdU assays also revealed that knockout of TBC1D8 and TBC1D14 both significantly downregulated the proliferation and migration abilities of HCC cells (Figs. 9C and 9D). These experiments further verified the fact that TBC1Ds promote HCC progression in vitro.